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. Some previous research already has established that these prediction models are generalizable in terms of their classification accuracy across different samples (e.g., Grice & Dugan, 2001, 2003; Grice & Ingram, 2001). That is, these studies show that the prediction models can detect company distress accurately, independent of the observation samples. But in addition to testing the generalizability of these prediction models across different samples, it also is necessary to test for re-estimations of the model coefficients (Grice & Dugan, 2003) and confirm the statistical significance of the prediction results (Grice & Dugan, 2001). Few tests of proposed prediction models include these research considerations though. Instead, most research tends to implement specific prediction models for individual country samples, to measure and compare their prediction performance (Kordlar & Nikbakht, 2011; Lifschutz & Jacobi, 2010; Oude, 2013; Pongsatat, Ramage, & Lawrence, 2004), or else apply original versions of the models without examining the statistical validity of their results (Almamy, Aston, & Ngwa, 2016; Chouhan, Chandra, & Gosvami, 2014; Hussain, Ali, Ullah, & Ali, 2014; Malik, Aftab, & Noreen, 2013; Mizan & Hossain, 2014). To extend beyond such considerations, the present study checks the effectiveness of the five models across an economically diverse, multicountry sample.