In the past three decades, China has made enormous progress in its economic development. With the development of Chinese economy, growth enterprises, particularly those enterprises that either have high technology or use good business ideas and growth potential, have become important in the industrialization process in China. Furthermore, the continued health of growth enterprises is essential to China’s global economic competitiveness (CSRC,2008). In order to provide a fund raising venue and an exit ground for high-growth and high-risk enterprises in all industries, the Hong Kong Exchanges and Clearing Limited (HKEx) established the Hong Kong Growth Enterprise Market (GEM) in 1999. The GEM has lowered the entry barriers to attract an increasing number of growth enterprises to capitalize on this market. There is no doubt that the GEMs with a lower entry threshold enable growth enterprises with growth potential but without a proven track record of performance to capitalize on the growth opportunities of China by raising expansion capital on a well-established market (Vong and Zhao, 2008). Nevertheless, the future performance of growth companies, particularly those without a profit track record, is susceptible to great uncertainty. Because of the high financial risk and imperfections in the financial constitution of growth enterprises, the investors are cautious about investing in GEM in Hong Kong and in the newly established GEM in mainland China (Chen, Sun and Zhang,2005). Therefore, it has become very important to develop a reliable financial distress prediction model which covers appropriate predictors to predict the financial distress of growth enterprises on the GEM. The present study, using the data of growth enterprises on Hong Kong GEM, made the first attempt to construct a financial distress prediction model for Chinese growth enterprises. The methods including Mann- Whitney-Wilcoxon (MWW),factor analysis and logistic regression, were then applied to analyse the data. One financial distress model which included financial factors and another financial distress model which included non-financial and macroeconomic factors were constructed in the method section. Based on these two models, the present study developed a financial distress prediction model, which used not only financial factors but also non-financial and macroeconomic factors. In the existing literature, financial variables (ratios or factors) were the most frequently used predictors in the models that forecast corporate financial distress. Some important research studies suggested they were the most important predictors for forecasting the financial distress (Altman,1968; Altman, Haldeman and Narayanan,1977; Ohlson,1980). In contrast, the present study’s findings are different and significant: the logistic regression model that included firm-specific non-financial and macroeconomic factors was better in predicting growth enterprises’ financial distress than the model which included firm-specific financial factors. Furthermore, the model incorporating firm-specific financial, firm-specific non-financial and macroeconomic factors was better than the model which included firm-specific financial factors in financial distress prediction. The investors or potential investors can benefit from these findings on financial distress prediction because these findings would enable them to better assess the probability of the growth enterprises experiencing financial distress in the near future.
|Date of Award||2011|
|Supervisor||Milind Sathye (Supervisor) & Craig Applegate (Supervisor)|