دانلود رایگان مقاله انگلیسی تعاریف مدل های پیش بینی ورشکستگی: شواهد از اقتصادهای نوظهور - الزویر 2018

Introduction

Even as modern researchers and practitioners recognize the critical need for more accurate bankruptcy and distress prediction models, a lack of consensus remains regarding how various proposed models perform in different economic circumstances. In particular, available bankruptcy prediction models might not generalize across economic environments, such as those that mark different nations. By scrutinizing the prediction capability of models across countries, the current study seeks to extend prior literature that tends to investigate prediction models only in relation to developed economies (e.g., Agarwal & Taffler, 2007, 2008; Boritz, Kennedy, & Sun, 2007). But such studies necessarily reflect the unique traits of their samples, suggesting the powerful demand for cross-country analyses of extant models (Altman, Iwanicz-Drozdowska, Laitinen, & Suvas, 2017), across economies that represent diverse settings. Furthermore, some prediction models fail to establish a firm theoretical basis for their financial ratio selections (Charitou, Neophytou, & Charalambous, 2004; Gentry, Newbold, & Whitford, 1985a; Grice & Dugan, 2003; Oz & Yelkenci, 2017), which could imply even greater sample dependence. To explore existing bankruptcy prediction models' generalizability, and in particular their applicability to emerging economies, this study focuses on five prominent models proposed by Altman (1968), Ohlson (1980), Taffler (1983), Zmijewski (1984), and Shumway (2001). All five of these models originally were derived with samples that came from developed economies, whereas their applicability to emerging economy samples has not been tested. Furthermore, the models originally applied to industrial firms, and the health of such firms is central to the efforts of emerging markets to participate in the global economy (Khanna & Palepu, 2006; Oz & Yelkenci, 2017). In this sense, confirming the generalizability of these models would provide pertinent insights for research but also hold promise for informing practitioners about which prediction models they should adopt.

Conclusion

This study contributes to extant literature by confirming the level of generalizability of some prominent prediction models. Some of these models produce successful distress predictions across different samples, times, and economic conditions. Therefore, they can serve as benchmarks for continued studies in emerging markets, enabling researchers to continue improving existing prediction models or establish new ones. In particular, the results of the current study confirm that, other than the Altman (1968) model, popular distress prediction models are generalizable to different samples and time periods. According to a detailed, five-stage examination process, practitioners in emerging markets should reestimate the Zmijewski (1984) and Shumway (2001) prediction models, because doing so leads to statistically significant improvements in all prediction tests (i.e., full and subsample outcomes and holdout sampling). In contrast, the Ohlson (1980) model provides high prediction accuracy in its original version, indicating that it is stationary in time and applicable to distinct time periods and economic conditions. Despite its high prediction accuracy for the full sample and the pre- and post- financial crisis periods, the holdout sample results for the Taffler (1983) model do not support improved prediction processes though, so researchers should use caution before applying this model to developing country samples. The current study results pertain to a broad range of MSCI emerging market countries, yet even in this case, the various economies feature specific financial and accounting infrastructures, relative to those in place in other developing countries. The results thus are valid for the publicly listed firms in these samples, and they should be examined for non-listed firms. Further studies could also examine more publicly listed firms in different developing countries, depending on the availability of the data needed to test the models' generalizability for additional samples.