外文翻譯---財(cái)務(wù)困境與破產(chǎn)的比較-wenkub
2023-05-19 08:44:23 本頁(yè)面
【正文】 f variables that predict bankruptcy have no predictive power regarding financial distress then a pletely new explanatory model is required. That inquiry is the objective of this paper. Methodology Sample selection and financial distress identification Combining many industries within a data set increases sample size, 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 specific coefficients are estimated. The industryrelative framework is one way to deal with the flexible coefficients problem and provides practical advantages arising from the use of a mon platform to predict an event across many industries. Altman and Izan (1984) pioneered industry relative ratios to normalize differences among industries in a bankruptcy study. Platt and Platt (1990, 1991) illustrated the conceptual benefits from using industryrelative ratios within the context of early warning system models and demonstrated the applicability of this framework using US firms. This paper uses the industryrelative framework as well but for financial distress prediction. that belonged to the 14 manufacturing industries listed in Table 1. Restricting the data to a single year circumvents estimation 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 pany listed on the COMPUSTAT tape for the 14 industries to avoid choicebased sample bias (See Zmijewski, 1984). Further, the industry relative approach used to create financial ratios for panies within the 14 industries insures a more than adequate sample size. Companies on the COMPUSTAT tape were bifurcated into financially distressed and solvent groups with a threepart system, over a twoyear period, 1999 to 2020. Financial distressed firms were defined as those that met each of the following screening criteria for both years. ? Negative EBITDA interest coverage (similar to Asquith, Gertner and Scharfstein (1994)). ? Negative EBIT (similar to John, Lang, and Netter (1992)). ? Negative ine before special items (similar to Hofer (1980)). To avoid defining panies as financially distressed based on a single year of poor performance, the three screens above were calculated for the years 1999 and 2020. 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 panies for the analysis sample, including 276 financially distressed firms and 1,127 nonfinancially distressed panies. Two other financial distress identifiers previously employed by researchers were not included in the screening system: cash flow less than current maturities of longterm debt and layoffs, restructurings, or missed dividend payments. In the former case, the variable was excluded because it necessitates omitting panies without longterm debt. The later metric was dropped because prehensive data were not available. The threescreen system produced a total of 276 cases of financial distress across 14 industries as seen in Table 1. The table also includes the percentage of financial distressed firms in
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