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Tuvadaratragool, S 2013, 'The role of financial ratios in signalling financial distress: evidence from Thai listed companies', DBA thesis, Southern Cross University, Lismore, NSW.

Copyright S Tuvadaratragool 2013


This thesis investigates the relationship between company characteristics and business failure among publicly listed companies on the Stock Exchange of Thailand during 2003–2008. The characteristics used are financial statement information/ratios. In other words, this thesis aims to examine whether financial statement ratios can be adequately used to signal business failure in the Thai context in normal economic circumstances.

The motivation behind this thesis derives from the literature, particularly as it applies to Thailand. This thesis used quarterly financial statement data so as to take into account seasonal/cyclical changes, which previous studies have ignored. The underlying reason for applying quarterly financial statement data is that it is believed that business failure is a dynamic process (Hossari 2006; and Shumway 2001). The methods used in this thesis are the Integrated Multi-Measure (IMM) approach (which comprises the Emerging Market Score model, comparative ratio analysis, and ratio trend analysis) and the logit model as a benchmarking measure. The successful classification rates of each individual measure are similar (approximately 57 per cent). The classification accuracy rate for the IMM approach is 64 per cent for the financially distressed firms while the logit model provides the classification accuracy rate of up to 86 per cent for the financially distressed firms.

The empirical results show that financial statement information can be used to adequately signal business failure in the Thai context in normal economic circumstances. This is consistent with the recent study by Beaver, McNichols and Rhie (2005) conducted in a western setting. Given this, the empirical results of this study could be used as a stepping stone for future researchers who are interested in finding the best predictors for failure by developing a ratio–based prediction model and assessing its classification accuracy.