外文翻譯---財務困境與破產的比較

1、<p>　　西 南 交 通 大 學</p><p><b>　　本科畢業(yè)論文</b></p><p>　　財務困境與破產的比較</p><p>　　COMPARING FINANCIAL DISTRESS AND BANKRUPTCY</p><p>　　年 級:2006 級</p>&l

2、t;p>　　學 號:20063027</p><p>　　姓 名:景 吉 英</p><p>　　專 業(yè):會 計</p><p>　　指導老師:段 宏</p><p><b>　　2010年06月</b></p><p><b>　　英文原文：<

3、/b></p><p>　　Comparing Financial Distress and Bankruptcy</p><p><b>　　Abstract</b></p><p>　　Most research purporting to address the issue of financial distress has act

4、ually studied samples of bankrupt companies. In contrast, this paper starts with a sample of companies that are financially distressed but not yet bankrupt. The sample was obtained following a screen of the Compustat ind

5、ustry database with a three-tiered identification system. The screen bifurcated companies into financially distressed and not distressed groups. A multi-tiered screen reduces the incidence of mistakenly id</p><

6、;p>　　Key Words: Financial Distress, Bankruptcy, Early Warning Model, and Renewal</p><p>　　Introduction</p><p>　　The ability to predict with reasonable accuracy companies likely to file for ba

7、nkruptcy protection benefits bank loan officers, investors, credit managers, regulators and vendors among others. These benefits principally accrue to participants in the end-stage of the corporate life cycle. That is, t

8、hese predictions come too late in the process of corporate decline to do much more than give a warning that the final phase of corporate existence is near. Whereas early bankruptcy prediction benefits </p><p&g

9、t;　　In most cases, bankruptcy occurs subsequent to a period of financial distress. Identification of healthy companies likely to become financially distressed would allow remedial actions to possibly correct the causes o

10、f corporate decline before bankruptcy ensued. In addition to benefiting the stakeholders listed above, earlier financial distress information would provide insights to managers and owners, and would increase confidence t

11、hat future deliveries will be made among the network of other co</p><p>　　The definition of financial distress is less precise than the legal actions that define proceedings such as bankruptcy or liquidation

12、. Despite this uncertainty, it is clear that the condition of being financially distressed deviates from corporate normality in a manner similar to bankruptcy.</p><p>　　Financial distress precedes virtually

13、all bankruptcies excepting those precipitated by sudden and unexpected events such as natural disasters, changed government regulations, or legal judgments. The question naturally arises whether the same factors known to

14、 be indicators of future bankruptcies are also indicators of future cases of financial distress. They may not be because bankruptcy is an event that recognized the failure of the company; in contrast, financial distress

15、happens in stages and </p><p>　　Methodology</p><p>　　Sample selection and financial distress identification</p><p>　　Combining many industries within a data set increases sample siz

16、e, which produces econometric advantages resulting from smaller standard errors of estimates. But coefficients may not be stable across industries, which lead to a proliferation of coefficient estimates if industry speci

17、fic coefficients are estimated. The industry-relative framework is one way to deal with the flexible coefficients problem and provides practical advantages arising from the use of a common platform to predict an even<

18、/p><p>　　The study included firms from the 2000 COMPUSTAT ?Industrial Annual tape that belonged to the 14 manufacturing industries listed in Table 1. Restricting the data to a single year circumvents estimation

19、 issues arising from variations in inflation rates, interest rates, and GDP growth rates as described by Mensah (1984) and Platt, Platt and Pedersen (1994). The sample includes every company listed on the COMPUSTAT tape

20、for the 14 industries to avoid choice-based sample bias (See Zmijewski, 1984). </p><p>　　Companies on the COMPUSTAT tape were bifurcated into financially distressed and solvent groups with a three-part syste

21、m, over a two-year period, 1999 to 2000. Financial distressed firms were defined as those that met each of the following screening criteria for both years.</p><p>　　Negative EBITDA interest coverage (similar

22、 to Asquith, Gertner and Scharfstein</p><p><b>　　(1994)).</b></p><p>　　Negative EBIT (similar to John, Lang, and Netter (1992)).</p><p>　　Negative net income before spec

23、ial items (similar to Hofer (1980)).</p><p>　　To avoid defining companies as financially distressed based on a single year of poor performance, the three screens above were calculated for the years 1999 and

24、2000. Companies were categorized as financially distressed if all three screens were negative in both years. Companies were defined as nonfinancially distressed otherwise. This approach yielded a total of 1403 companies

25、for the analysis sample, including 276 financially distressed firms and 1,127 nonfinancially distressed companies.</p><p>　　Two other financial distress identifiers previously employed by researchers were no

26、t included in the screening system: cash flow less than current maturities of long-term debt and layoffs, restructurings, or missed dividend payments. In the former case, the variable was excluded because it necessitates

27、 omitting companies without long-term debt. The later metric was dropped because comprehensive data were not available.</p><p>　　The three-screen system produced a total of 276 cases of financial distress ac

28、ross 14 industries as seen in Table 1. The table also includes the percentage of financial distressed firms in each industry and the number of no n-distressed companies. The requirement that companies fall below all thre

29、e financial distress screen thresholds means that they are in a serious though not necessarily a fatal phase of distress. This methodology yields relatively more cases of financial distress in the Indus</p><p&

30、gt;　　The comparison group of 1,127 non-distressed companies includes all companies in COMPUSTAT in the 14 industries that are not already identified as financially distressed and that have complete data for 1999 and 2000

31、. Financially distressed firms are arbitrarily assigned a value of 1, while healthy firms are assigned a value of 0. The ability of a model to differentiate between populations of companies is affected by the degree to w

32、hich the groups differ. The continuum of corporate health has a h</p><p>　　Independent Variables</p><p>　　Independent variables were created from financial statement data obtained from COMPUSTAT

33、 for the year 1999. Data from 1999 precedes by twelve months the date when companies are identified as financially distressed, which allows them to be used in constructing an early warning model of financial distress. Da

34、ta selection includes typical financial statement items.</p><p>　　Table 3 lists the specific financial items taken from Compustat and the financial ratios formed to measure profitability, liquidity, operatio

35、nal efficiency, leverage and growth. These ratios are tested as possible determinants of financial distress.</p><p>　　The transformation of company ratios into industry-relative ratios is described in equati

36、on (1). </p><p>　　Industry - RelativeRatioi,j??????????????（1）</p><p>　　where firm i is a member of industry j and 100 adjusts percentage ratios to scalar values greater than 1.0. The transform

37、ation starts with a company’s ratio and then divides that quotient by the value of that same ratio for the average firm in the industry. Industry-relative ratios combine changes occurring at individual companies and acro

38、ss their aggregate industry. They reveal when a company’s ratio deviates from its industry norm. Industry relative advocates such as Lev (1969) and Platt and P</p><p>　　Conclusion</p><p>　　Alter

39、nate means of identifying companies in financial distress have been proposed by a variety of researchers. We show that a three-screen criteria combining several previously proposed definitions yields equivalent or lower

40、model standard errors than any one or two criteria screening models. With this bifurcation of companies, an industry relative early warning model of financial distress, not bankruptcy, was built using data for 14 industr

41、ies. The classification results suggest that it may be </p><p>　　A second inquiry compared the financial distress model to a previously estimated bankruptcy prediction model. Statistical tests reject the hyp

42、othesis that financial distress and bankruptcy are same process. This partially explains why many financially distressed firms do not ultimately file for bankruptcy protection.</p><p><b>　　文獻翻譯：</b&

43、gt;</p><p>　　財務困境與破產的比較</p><p><b>　　摘要</b></p><p>　　從大多數解決財務困境的問題的研究來看，實際上是以破產企業(yè)作為研究樣本的。與此不同的是，本文開始于一個處于財務危機但尚未破產的企業(yè)的樣本。該樣本是從標準普爾行業(yè)數據庫中的信用等級三識別系統(tǒng)經過篩選得到的。該篩選將公司分為財務困境企業(yè)和

44、非財務困境企業(yè)兩部分。一個多層次的篩選降低了將一個非財務困境公司錯誤識別為財務危機困境公司的概率。本文接著對是否存在相同的解釋因子對破產和財務困境同時產生關聯(lián)提出疑問。于是，一個被建立的財務困境預警模型與已經存在的破產模型進行了比較。最終的財務危機預警模型包括一個已經存在于破產模型中的變量和4個新的變量。兩個模型之間解釋因子的部分重疊表明了財務困境和破產之間具有較強聯(lián)系，因為某些因素導致公司陷入財務困境而后來沒有導致公司破產。這項研究表

45、明，銀行和其他想要控制不</p><p>　　良貸款的債權人應該要依靠比知名的破產模型中包含的更多的信息。</p><p>　　關鍵詞：財務困境；破產；預警模型；重建</p><p><b>　　導論</b></p><p>　　能夠合理準確地預測公司可能申請破產保護有益于銀行信貸員，投資者，信貸管理人員，管理人員和供應

46、商等。這些好處主要歸于企業(yè)生命周期最終階段的參與者。也就是說，這些在企業(yè)衰退過程中的預測來得晚，而在企業(yè)臨近最后階段不能給予充分的預警。而早期的破產預測有利于企業(yè)重組和破產過程中的參與者，它為管理者或者是有職責扭轉一個處于危機或財務困難的企業(yè)的董事會提供了一些幫助。事實上，一個解釋破產預測模型的成功應用的關鍵因素是，申請第11章保護的企業(yè)通常在事件的一段時間之前就羅列出財務危機的表現征兆。 在大多數情況下，破產發(fā)生于繼財務危機之

47、后的一段時間。健康企業(yè)的識別可能成為財務困境可以采取的補救措施，該措施有可能糾正在破產發(fā)生之前導致企業(yè)衰退的原因。除了有利于上面列出的股東外，早期的財務困境的信息將為管理者和所有者提供見解，并會為通過企業(yè)供應鏈來構成和其他網絡相關企業(yè)的未來交貨增加信心。最重要的是，這些信息將使財務困難企業(yè)被處理，并可能被治愈，而不是任其破產。 財務困境的定義不如法律行動，如破產或清算程序的定義精確。盡管存在這種不確定性，但很明顯的是，企業(yè)處于財

48、務困境的狀態(tài)也與被描述為相似</p><p>　　除了諸如自然災害，政府規(guī)章改變或者法律判決這些突發(fā)事件之外，財務困境都先于幾乎所有的破產。不管同樣的已知因素是否既是未來破產的指標也是未來財務困境狀況的指標，該問題已經顯而易見出現了。他們可能不會成為一個認定公司倒閉的事件而導致破產；與此不同的是，財務困境分階段和不同程度而發(fā)生。如果這兩個過程是相關聯(lián)的，那么破產預測模型的變型可能會產生財務困境的預測；或者，如果預

49、測破產變量沒有關于財務困境的預測能力，那么一個全新的解釋模式是必需的。這就是本文的研究目的。</p><p><b>　　研究方法</b></p><p>　　樣本的選取和財務困境的識別</p><p>　　結合許多行業(yè)到一個數據集里來增加樣本量，將產生由較小的標準估計誤差引起的計量經濟學的優(yōu)勢。但跨行業(yè)間的系數可能不穩(wěn)定，如果行業(yè)特定系數被估

50、計將導致系數估計的擴散。該行業(yè)的相對框架是解決彈性系數問題的一種方法，并為用一個共同平臺預測很多行業(yè)間事件提供實際優(yōu)勢。Altman 和 Izan（1984年）開創(chuàng)的行業(yè)相對比率將規(guī)范破產研究的行業(yè)之間存在的差異。Platt 和 Platt（1990年和1991年）說明了在早期預警系統(tǒng)模型范圍內使用行業(yè)相對比率的理論上的優(yōu)勢，并論證了此框架應用于美國公司的適用性。本文也采用了行業(yè)的相對框架，但是為了研究財務困境的預測。</p>

51、;<p>　　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2000年行業(yè)標準普爾數據庫的年度數據。如Mensah (1984年) ，Platt, Platt 和Pedersen (1994年)所描述，將數據限制到一年規(guī)避了由于通貨膨脹率，利率和國內生產總值增長率的變化所導致的估計問題。該樣本包括了14個制造行業(yè)標準普爾數據庫中列示的每個公司，以此來避免抽樣偏差（見Zmijewski，1984年）。

52、此外，將與企業(yè)相對接近的財務比率用于14個行業(yè)內公司財務比率的配對以確保樣本規(guī)模的充足。</p><p>　　標準普爾數據庫中記錄的公司被三分體系以兩年為期間（1999到2000年）分為財務困境樣本和有償債能力的樣本兩部分。財務困境企業(yè)被定義為兩個年度內同時滿足以下標準的企業(yè)。</p><p>　　息稅折舊攤銷前利潤的利息保障率為負（類似于Asquith, Gertner和Scharfst

53、ein（1994年））。</p><p>　　息稅前利潤為負（類似于 John, Lang, 和 Netter (1992年)）。</p><p>　　特殊項目之前的前凈收入為負（類似于Hofer (1980年)）。</p><p>　　為了避免基于單年表現欠佳而將企業(yè)定義為財務困境企業(yè)，上述的三屏是為了適合1999年和2000年而設計的。如果所有三屏在這兩年為負，

54、則公司被歸類為財務困境企業(yè)。否則公司就會被定義非財務困境企業(yè)。這種方法將1403家公司為分析樣本，其中包括276家財務困境企業(yè)和1127家非財務困境企業(yè)。</p><p>　　另外兩個以前被研究人員采用但不包括在篩選系統(tǒng)的財務困境標識是：現金流量小于一年到期的長期債務和臨時裁員，重組，或錯過的分紅。在前者情況下的變量須被排除，因為它需要刪除沒有長期債務的公司。后者的度量被取消，因為無法獲得全面的數據。</p

55、><p>　　這三屏系統(tǒng)產生了14個行業(yè)的總數為276的財務困境樣本，如表1所示。該表還包括各行業(yè)的財務困境企業(yè)的百分比和非財務困境企業(yè)的數目。公司低于所有三個財務困境屏幕閾值的要求意味著他們是在一個嚴重但不一定是一個致命的困境階段。這種方法在工業(yè)機械、設備、 儀器及相關產品中產生的財務困境樣本比其他 12 行業(yè)要多。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2000年的商業(yè)媒體中。三層篩選方法如何影

56、響被標識為財務困境企業(yè)的數目見表 2 中，表2對單個和多個屏幕進行了比較。根據定義的三屏幕系統(tǒng)產生了最少的財務困境樣本，因為它是一個公司的各個屏幕相交處的蒸餾。根據定義的三屏系統(tǒng)產生的財務困難最少的情況下，因為它是公司在各個屏幕路口升華。多個屏幕方法通過1.4%到18.3%的十四個行業(yè)之間來減少財務困境企業(yè)的數目，并與一種當任何一個屏被違反時就稱為財務困境的方法對比。在這三個獨立的屏之中，企業(yè)財務困境種類的選擇，屏3是最浪費的，然而屏1

57、是最經濟的。三個不同財務困境屏幕的重疊比預想還少，如表2所示。被大量財務困境公司證明，屏3是最不典型的，且不同于被識別的其他兩個屏。</p><p>　　1127個非財務困境企業(yè)的對照組包括了14個行業(yè)標準普爾數據庫的所有企業(yè)，這些企業(yè)未被識別為財務困境，并且具有1999年和2000年的完整數據。財務困境企業(yè)被任意指定為1值，而財務正常企業(yè)被指定為0值。一個模型區(qū)分公司種類的能力是受分類程度上不同的影響的。企業(yè)健

58、康持續(xù)發(fā)展一個方面是健康種類，另一個方面則為破產種類，而財務困境則居于兩者之間。因此，財務困境/健康配對比破產/健康配對更相近，這表明相比預測破產而言，預測財務困境更難。</p><p><b>　　自變量</b></p><p>　　自變量的建立是從1999年的標準普爾數據庫的財務報表數據中得到的。追溯到1999當公司被認定為處于財務困境之前的12個月的數據，這使建

59、立財務困境預警模型成為可能。數據選擇包括典型的財務報表項目。</p><p>　　表3列出了從標準普爾數據庫獲取的具體財務項目和為衡量盈利能力，流動性，運營效率，利用和增長而產生的財務比率。這些作為財務困境可能因素的比率是被測試。</p><p>　　將公司比率轉換為行業(yè)相對比率的方程如方程（1）所示。</p><p>　　行業(yè)相對比率ij=*100 （1）&l

60、t;/p><p>　　其中，公司i是行業(yè)j的成員之一， 用100調整百分比率以使標值大于1.0。轉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。行業(yè)相對比率的組合變化發(fā)生于個別公司和其聚合的整個行業(yè)。當一個公司的比率偏離行業(yè)標準時，他們將會表現出來。行業(yè)相對的擁護者，如Lev (1969年) 與 Platt 和 Platt (1991年)認為，這些比率更穩(wěn)定并能導致事前和事后之間的預測差距減小。它們還提供了一個概

61、念框架，其中每個行業(yè)并不需要一套獨特的參數估計。在整篇文件中，行業(yè)相對表示是被抑制為簡化表示法。</p><p><b>　　結論</b></p><p>　　識別公司財務困境的替代辦法是由許多研究人員提出的。我們證明了結合幾個先前提議定義的一個三屏幕標準帶來比任何一個或兩個標準屏模型相當甚至更低的模型標準錯誤。由于企業(yè)的這種分歧，一個行業(yè)的財務困境（而不是破產）相對

62、預警模型，通過使用14個行業(yè)的數據被建成了。分類結果表明，通過采取糾正措施來減輕破壞生產之前的財務困境是可能的。區(qū)分財務困境企業(yè)和健康企業(yè)比傳統(tǒng)地比較破產和健康企業(yè)更難，因此建立預警系統(tǒng)模型來檢測財務困境也就更加困難了。</p><p>　　相比先前估計的破產預測模型，財務困境模型是一個補充研究。統(tǒng)計檢驗拒絕財務困境和破產是相同的過程的這一假設。這也從一個角度解釋了為什么許多財務困境企業(yè)并沒有最終申請破產保護。&