tailieunhanh - Using accounting ratios in predicting financial distress: An empirical investigation in the Vietnam stock market

Using accounting ratios in predicting financial distress: An empirical investigation in the Vietnam stock market. Financial distress prediction is an important and practical research topic for many stakeholders and has attracted extensive studies over the past decades. | Journal of Economics and Development, , , April 2015, pp. 41-49 ISSN 1859 0020 Using Accounting Ratios in Predicting Financial Distress: An Empirical Investigation in the Vietnam Stock Market Vo Xuan Vinh University of Economics Ho Chi Minh City, Vietnam CFVG Ho Chi Minh City, Vietnam Email: vinhvx@ Abstract Financial distress prediction is an important and practical research topic for many stakeholders and has attracted extensive studies over the past decades. This paper investigates the challenging issue of financial distress in Vietnam by distinguishing “healthy” companies from “financially distressed” companies using a data sample of firms listed on the Ho Chi Minh City Stock Exchange. Employing the logistic regression model to predict financial distress with a unique data set, we characterize the determinants of financial distress in terms of firm accounting and financial ratios over the period from 2007 to 2012. The results indicate that financial ratios can be employed as an early warning of financial distress as financial ratios are significantly correlated with the probability of firm financial distress. Keywords: Financial distress; insolvency; logit. Journal of Economics and Development 41 Vol. 17, , April 2015 1. Introduction terest in the insolvent firms. The failure of a firm involves many parties with huge costs. Therefore, research focusing on corporate failure prediction providing a better understanding on the topic has attracted interest from many stakeholders, including not only academic but also private agents and government. The recent bankruptcies of many large corporations all over the world have underlined the importance of default prediction both in academia and in industry (Hol et al., 2002). These catastrophic corporate failures highlight the need to develop early warning systems that can help prevent or avert corporate defaults. This also facilitates the investment selection of firms to collaborate with